Dynamic rerouting of wireless traffic based on input from machine learning-based mobility path analysis

ABSTRACT

In one embodiment, a service receives data indicative of roaming failures along mobility paths in a network. The mobility paths represent ordered series of wireless access points via which wireless clients have accessed the network over time. The service uses, based on the data indicative of the roaming failures, a machine learning-based model to associate mobility path failure metrics with portions of the mobility paths. The service identifies, for a first mobility path, an alternate mobility path that has a lower mobility path failure metric than that of the first mobility path. The service triggers a mobility path reroute for a particular client device in the network on the first mobility path to the alternate mobility path.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to the dynamic rerouting of wireless traffic based oninput from machine learning-based mobility path analysis.

BACKGROUND

In most wireless networks, such as Wi-Fi networks, roaming is a fairlycommon event. Generally, roaming refers to a client device transitioningfrom one wireless access point (AP) to another. Notably, roaming isoften caused by the client device attempting to connect to the “best” APavailable in the location of the client. The “best” AP from thestandpoint of the client device may change over time due to movement ofthe client, changes in the environment that affect the signal (e.g., interms of strength, signal to noise ratio, etc.), or other such factors.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example network assurance system;

FIGS. 4A-4E illustrate examples of a mobility path in a network;

FIG. 5 illustrates an example architecture for performing mobility pathanalysis in a network assurance system;

FIG. 6 illustrates examples of mobility paths in three dimensions;

FIGS. 7A-7B illustrate examples of an assessment of client trajectories;and

FIG. 8 illustrates an example simplified procedure for triggering amobility path reroute by a client device.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a servicereceives data indicative of roaming failures along mobility paths in anetwork. The mobility paths represent ordered series of wireless accesspoints via which wireless clients have accessed the network over time.The service uses, based on the data indicative of the roaming failures,a machine learning-based model to associate mobility path failuremetrics with portions of the mobility paths. The service identifies, fora first mobility path, an alternate mobility path that has a lowermobility path failure metric than that of the first mobility path. Theservice triggers a mobility path reroute for a particular client devicein the network on the first mobility path to the alternate mobilitypath.

DESCRIPTION

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise a networkassurance process 248, as described herein, any of which mayalternatively be located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Network assurance process 248 includes computer executable instructionsthat, when executed by processor(s) 220, cause device 200 to performnetwork assurance functions as part of a network assuranceinfrastructure within the network. In general, network assurance refersto the branch of networking concerned with ensuring that the networkprovides an acceptable level of quality in terms of the user experience.For example, in the case of a user participating in a videoconference,the infrastructure may enforce one or more network policies regardingthe videoconference traffic, as well as monitor the state of thenetwork, to ensure that the user does not perceive potential issues inthe network (e.g., the video seen by the user freezes, the audio outputdrops, etc.).

In some embodiments, network assurance process 248 may use any number ofpredefined health status rules, to enforce policies and to monitor thehealth of the network, in view of the observed conditions of thenetwork. For example, one rule may be related to maintaining the serviceusage peak on a weekly and/or daily basis and specify that if themonitored usage variable exceeds more than 10% of the per day peak fromthe current week AND more than 10% of the last four weekly peaks, aninsight alert should be triggered and sent to a user interface.

Another example of a health status rule may involve client transitionevents in a wireless network. In such cases, whenever there is a failurein any of the transition events, the wireless controller may send areason_code to the assurance system. To evaluate a rule regarding theseconditions, the network assurance system may then group 150 failuresinto different “buckets” (e.g., Association, Authentication, Mobility,DHCP, WebAuth, Configuration, Infra, Delete, De-Authorization) andcontinue to increment these counters per service set identifier (SSID),while performing averaging every five minutes and hourly. The system mayalso maintain a client association request count per SSID every fiveminutes and hourly, as well. To trigger the rule, the system mayevaluate whether the error count in any bucket has exceeded 20% of thetotal client association request count for one hour.

In various embodiments, network assurance process 248 may also utilizemachine learning techniques, to enforce policies and to monitor thehealth of the network. In general, machine learning is concerned withthe design and the development of techniques that take as inputempirical data (such as network statistics and performance indicators),and recognize complex patterns in these data. One very common patternamong machine learning techniques is the use of an underlying model M,whose parameters are optimized for minimizing the cost functionassociated to M, given the input data. For instance, in the context ofclassification, the model M may be a straight line that separates thedata into two classes (e.g., labels) such that M=a*x+b*y+c and the costfunction would be the number of misclassified points. The learningprocess then operates by adjusting the parameters a,b,c such that thenumber of misclassified points is minimal. After this optimization phase(or learning phase), the model M can be used very easily to classify newdata points. Often, M is a statistical model, and the cost function isinversely proportional to the likelihood of M, given the input data.

In various embodiments, network assurance process 248 may employ one ormore supervised, unsupervised, or semi-supervised machine learningmodels. Generally, supervised learning entails the use of a training setof data, as noted above, that is used to train the model to apply labelsto the input data. For example, the training data may include samplenetwork observations that do, or do not, violate a given network healthstatus rule and are labeled as such. On the other end of the spectrumare unsupervised techniques that do not require a training set oflabels. Notably, while a supervised learning model may look forpreviously seen patterns that have been labeled as such, an unsupervisedmodel may instead look to whether there are sudden changes in thebehavior. Semi-supervised learning models take a middle ground approachthat uses a greatly reduced set of labeled training data.

Example machine learning techniques that network assurance process 248can employ may include, but are not limited to, nearest neighbor (NN)techniques (e.g., k-NN models, replicator NN models, etc.), statisticaltechniques (e.g., Bayesian networks, etc.), clustering techniques (e.g.,k-means, mean-shift, etc.), neural networks (e.g., reservoir networks,artificial neural networks, etc.), support vector machines (SVMs),logistic or other regression, Markov models or chains, principalcomponent analysis (PCA) (e.g., for linear models), multi-layerperceptron (MLP) ANNs (e.g., for non-linear models), replicatingreservoir networks (e.g., for non-linear models, typically for timeseries), random forest classification, or the like.

The performance of a machine learning model can be evaluated in a numberof ways based on the number of true positives, false positives, truenegatives, and/or false negatives of the model. For example, the falsepositives of the model may refer to the number of times the modelincorrectly predicted whether a network health status rule was violated.Conversely, the false negatives of the model may refer to the number oftimes the model predicted that a health status rule was not violatedwhen, in fact, the rule was violated. True negatives and positives mayrefer to the number of times the model correctly predicted whether arule was violated or not violated, respectively. Related to thesemeasurements are the concepts of recall and precision. Generally, recallrefers to the ratio of true positives to the sum of true positives andfalse negatives, which quantifies the sensitivity of the model.Similarly, precision refers to the ratio of true positives the sum oftrue and false positives.

FIG. 3 illustrates an example network assurance system 300, according tovarious embodiments. As shown, at the core of network assurance system300 may be a cloud service 302 that leverages machine learning insupport of cognitive analytics for the network, predictive analytics(e.g., models used to predict user experience, etc.), troubleshootingwith root cause analysis, and/or trending analysis for capacityplanning. Generally, architecture 300 may support both wireless andwired network, as well as LLNs/IoT networks.

In various embodiments, cloud service 302 may oversee the operations ofthe network of an entity (e.g., a company, school, etc.) that includesany number of local networks. For example, cloud service 302 may overseethe operations of the local networks of any number of branch offices(e.g., branch office 306) and/or campuses (e.g., campus 308) that may beassociated with the entity. Data collection from the various localnetworks/locations may be performed by a network data collectionplatform 304 that communicates with both cloud service 302 and themonitored network of the entity.

The network of branch office 306 may include any number of wirelessaccess points 320 (e.g., a first access point AP1 through nth accesspoint, APn) through which endpoint nodes may connect. Access points 320may, in turn, be in communication with any number of wireless LANcontrollers (WLCs) 326 located in a centralized datacenter 324. Forexample, access points 320 may communicate with WLCs 326 via a VPN 322and network data collection platform 304 may, in turn, communicate withthe devices in datacenter 324 to retrieve the corresponding networkfeature data from access points 320, WLCs 326, etc. In such acentralized model, access points 320 may be flexible access points andWLCs 326 may be N+1 high availability (HA) WLCs, by way of example.

Conversely, the local network of campus 308 may instead use any numberof access points 328 (e.g., a first access point AP1 through nth accesspoint APm) that provide connectivity to endpoint nodes, in adecentralized manner. Notably, instead of maintaining a centralizeddatacenter, access points 328 may instead be connected to distributedWLCs 330 and switches/routers 332. For example, WLCs 330 may be 1:1 HAWLCs and access points 328 may be local mode access points, in someimplementations.

To support the operations of the network, there may be any number ofnetwork services and control plane functions 310. For example, functions310 may include routing topology and network metric collection functionssuch as, but not limited to, routing protocol exchanges, pathcomputations, monitoring services (e.g., NetFlow or IPFIX exporters),etc. Further examples of functions 310 may include authenticationfunctions, such as by an Identity Services Engine (ISE) or the like,mobility functions such as by a Connected Mobile Experiences (CMX)function or the like, management functions, and/or automation andcontrol functions such as by an APIC-Enterprise Manager (APIC-EM).

During operation, network data collection platform 304 may receive avariety of data feeds that convey collected data 334 from the devices ofbranch office 306 and campus 308, as well as from network services andnetwork control plane functions 310. Example data feeds may comprise,but are not limited to, management information bases (MIBS) with SimpleNetwork Management Protocol (SNMP)v2, JavaScript Object Notation (JSON)Files (e.g., WSA wireless, etc.), NetFlow/IPFIX records, logs reportingin order to collect rich datasets related to network control planes(e.g., Wi-Fi roaming, join and authentication, routing, QoS, PHY/MACcounters, links/node failures), traffic characteristics, and other suchtelemetry data regarding the monitored network. As would be appreciated,network data collection platform 304 may receive collected data 334 on apush and/or pull basis, as desired. Network data collection platform 304may prepare and store the collected data 334 for processing by cloudservice 302. In some cases, network data collection platform may alsoanonymize collected data 334 before providing the anonymized data 336 tocloud service 302.

In some cases, cloud service 302 may include a data mapper andnormalizer 314 that receives the collected and/or anonymized data 336from network data collection platform 304. In turn, data mapper andnormalizer 314 may map and normalize the received data into a unifieddata model for further processing by cloud service 302. For example,data mapper and normalizer 314 may extract certain data features fromdata 336 for input and analysis by cloud service 302.

In various embodiments, cloud service 302 may include a machinelearning-based analyzer 312 configured to analyze the mapped andnormalized data from data mapper and normalizer 314. Generally, analyzer312 may comprise a power machine learning-based engine that is able tounderstand the dynamics of the monitored network, as well as to predictbehaviors and user experiences, thereby allowing cloud service 302 toidentify and remediate potential network issues before they happen.

Machine learning-based analyzer 312 may include any number of machinelearning models to perform the techniques herein, such as for cognitiveanalytics, predictive analysis, and/or trending analytics as follows:

Cognitive Analytics Model(s):

-   -   The aim of cognitive analytics is to find behavioral patterns in        complex and unstructured datasets. For the sake of illustration,        analyzer 312 may be able to extract patterns of Wi-Fi roaming in        the network and roaming behaviors (e.g., the “stickiness” of        clients to APs 320, 328, “ping-pong” clients, the number of        visited APs 320, 328, roaming triggers, etc). Analyzer 312 may        characterize such patterns by the nature of the device (e.g.,        device type, OS) according to the place in the network, time of        day, routing topology, type of AP/WLC, etc., and potentially        correlated with other network metrics (e.g., application, QoS,        etc.). In another example, the cognitive analytics model(s) may        be configured to extract AP/WLC related patterns such as the        number of clients, traffic throughput as a function of time,        number of roaming processed, or the like, or even end-device        related patterns (e.g., roaming patterns of iPhones, IoT        Healthcare devices, etc.).

Predictive Analytics Model(s):

-   -   These model(s) may be configured to predict user experiences,        which is a significant paradigm shift from reactive approaches        to network health. For example, in a Wi-Fi network, analyzer 312        may be configured to build predictive models for the        joining/roaming time by taking into account a large plurality of        parameters/observations (e.g., RF variables, time of day, number        of clients, traffic load, DHCP/DNS/Radius time, AP/WLC loads,        etc.). From this, analyzer 312 can detect potential network        issues before they happen. Furthermore, should abnormal joining        time be predicted by analyzer 312, cloud service 312 will be        able to identify the major root cause of this predicted        condition, thus allowing cloud service 302 to remedy the        situation before it occurs. The predictive analytics model(s) of        analyzer 312 may also be able to predict other metrics such as        the expected throughput for a client using a specific        application. In yet another example, the predictive analytics        model(s) may predict the user experience for voice/video quality        using network variables (e.g., a predicted user rating of 1-5        stars for a given session, etc.), as function of the network        state. As would be appreciated, this approach may be far        superior to traditional approaches that rely on a mean opinion        score (MOS). In contrast, cloud service 302 may use the        predicted user experiences from analyzer 312 to provide        information to a network administrator or architect in real-time        and enable closed loop control over the network by cloud service        302, accordingly. For example, cloud service 302 may signal to a        particular type of endpoint node in branch office 306 or campus        308 (e.g., an iPhone, an IoT healthcare device, etc.) that        better QoS will be achieved if the device switches to a        different AP 320 or 328.

Trending Analytics Model(s):

-   -   The trending analytics model(s) may include multivariate models        that can predict future states of the network, thus separating        noise from actual network trends. Such predictions can be used,        for example, for purposes of capacity planning and other        “what-if” scenarios.

Machine learning-based analyzer 312 may be specifically tailored for usecases in which machine learning is the only viable approach due to thehigh dimensionality of the dataset and patterns cannot otherwise beunderstood and learned. For example, finding a pattern so as to predictthe actual user experience of a video call, while taking into accountthe nature of the application, video CODEC parameters, the states of thenetwork (e.g., data rate, RF, etc.), the current observed load on thenetwork, destination being reached, etc., is simply impossible usingpredefined rules in a rule-based system.

Unfortunately, there is no one-size-fits-all machine learningmethodology that is capable of solving all, or even most, use cases. Inthe field of machine learning, this is referred to as the “No FreeLunch” theorem. Accordingly, analyzer 312 may rely on a set of machinelearning processes that work in conjunction with one another and, whenassembled, operate as a multi-layered kernel. This allows networkassurance system 300 to operate in real-time and constantly learn andadapt to new network conditions and traffic characteristics. In otherwords, not only can system 300 compute complex patterns in highlydimensional spaces for prediction or behavioral analysis, but system 300may constantly evolve according to the captured data/observations fromthe network.

Cloud service 302 may also include output and visualization interface318 configured to provide sensory data to a network administrator orother user via one or more user interface devices (e.g., an electronicdisplay, a keypad, a speaker, etc.). For example, interface 318 maypresent data indicative of the state of the monitored network, currentor predicted issues in the network (e.g., the violation of a definedrule, etc.), insights or suggestions regarding a given condition orissue in the network, etc. Cloud service 302 may also receive inputparameters from the user via interface 318 that control the operation ofsystem 300 and/or the monitored network itself. For example, interface318 may receive an instruction or other indication to adjust/retrain oneof the models of analyzer 312 from interface 318 (e.g., the user deemsan alert/rule violation as a false positive).

In various embodiments, cloud service 302 may further include anautomation and feedback controller 316 that provides closed-loop controlinstructions 338 back to the various devices in the monitored network.For example, based on the predictions by analyzer 312, the evaluation ofany predefined health status rules by cloud service 302, and/or inputfrom an administrator or other user via input 318, controller 316 mayinstruct an endpoint client device, networking device in branch office306 or campus 308, or a network service or control plane function 310,to adjust its operations (e.g., by signaling an endpoint to use aparticular AP 320 or 328, etc.).

As noted above, in many wireless networks, such as Wi-Fi networks,roaming is a fairly common event. Roaming can be triggered by mobilityof the client device whereby the client tries to always connect to thebest Access point (AP) or, sometimes, simply because the clientdetermines that the current AP is not the best AP. Notably, even whenthe client is not currently moving, signal strength, signal to noiseratio (SNR), etc. may change (e.g., due to changing environmentalconditions. In almost all forms of wireless networks, with the exceptionof DETNET and the like, roaming decisions are made by the client device.

The process of roaming in a wireless network is by far not “free” andcould be highly subject to issues, thus leading to connectivity loss andapplication disruption. There are different types of roaming (e.g.,intra-WLC, layer-2, layer-3, etc.) that potentially require a series ofsteps to successfully complete for the roaming to succeed: association,(re)authentication, rekeying the Group Temporal Key (GTK),de-authentication, DHCP operations, and the like. In other words, thereare a number of possible ways in which roaming can fail in a wirelessnetwork. Accordingly, the large number of roaming events that typicallyoccur in a wireless network, as well as the numerous conditions that canlead to roaming failures, can often impinge on the user experience.

Dynamic Rerouting of Wireless Traffic Based on Input from MachineLearning-Based Mobility Path Analysis

The techniques herein introduce a mechanism that helps to reduce and/oreliminate roaming failures in a wireless network. In some aspects, thetechniques herein introduce the concept of mobility path metrics used toevaluate the risk of failure when roaming in a wireless network. Inanother aspect, a central path computation engine (PCE) may gatherinformation regarding the roaming events in the network and leveragemachine learning to compute and associate a mobility failure metric withthe mobility path. Such a metric may then be used as a signal toconstrain the mobility paths based on their risk of failure. In furtheraspects, information regarding the mobility paths and their failuremetrics may be provided to client devices on request, such as when theclient joins the wireless network or on detection of a special event(e.g., to perform a fast reroute using the techniques herein). Note thatalthough the techniques herein are described primarily with respect toWi-Fi networks, the techniques herein are equally applicable in othernetworks such as, but not limited to, cellular network (e.g., 4G, 5G,LTE, etc.), IoT networks that use 802.15.4 with the techniques hereinadapted to take into account the local ETX of such links, and any otherwireless network that supports roaming.

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a service receives data indicative of roamingfailures along mobility paths in a network. The mobility paths representordered series of wireless access points via which wireless clients haveaccessed the network over time. The service uses, based on the dataindicative of the roaming failures, a machine learning-based model toassociate mobility path failure metrics with portions of the mobilitypaths. The service identifies, for a first mobility path, an alternatemobility path that has a lower mobility path failure metric than that ofthe first mobility path. The service triggers a mobility path reroutefor a particular client device in the network on the first mobility pathto the alternate mobility path.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with thenetwork assurance process 248, which may include computer executableinstructions executed by the processor 220 (or independent processor ofinterfaces 210) to perform functions relating to the techniquesdescribed herein.

Operationally, the concept of a mobility path in a network is introducedherein. In contrast with a data plane path, a mobility path generallyrefers to the list of APs that a client device visits/attaches to in agiven period. As would be appreciated, an access point may be a Wi-FiAP, a gateway in the context of the IoT, a base station, or any othernetworking device that communicates wirelessly with a client device andprovides the client device access to the wireless network. In somecases, a given mobility path may be defined as an ordered set of threeor more AP nodes. In other words, a mobility path is a control planepath that is not followed by the data, but by the client device itself.Accordingly, a mobility path failure herein generally refers to anunsuccessful roaming event. Such failed roaming events often lead totraffic disruptions, similar to a data path failure.

FIGS. 4A-4E illustrate examples of a mobility path in a network,according to various embodiments. In FIG. 4A, assume that a clientdevice 402 is a mobile device that is traveling along a path of travel406. As would be appreciated, while path of travel 406 is depicted as alinear path, the movement of a mobile device in most situations will notbe linear and may vary in one, two, or even three dimensions. Forpurposes of illustration, assume that the local network comprises APs404, such as APs 404 a-404 c (e.g., APs A-C), as shown. At time T=t₀,client device 402 may be connected to the wireless network via AP 404 a,which may be the closest AP 404 to client device 402 at this time or,alternatively, offer the best characteristics in terms of signalstrength, SNR, etc.

In FIG. 4B, assume now that client device 402 has moved along path oftravel 406 and is now closer to AP 404 b at time T=t₁. If the wirelesscharacteristics of AP 404 b, from the standpoint of client device 402,are now better than that of AP 404 a, client device 402 may initiateroaming. As a result, client device 402 may attach itself to AP 404 band detach itself from AP 404 a, thereby completing the roamingoperation. After attaching to AP 404 b, client device 402 may continueto communicate with the network as normal via AP 404 b.

In FIG. 4C, client device 402 may perform a similar operation as in FIG.4C, but with AP 404 c. Notably, assume now that at time T=t₂, clientdevice 402 is now within closest proximity to AP 404 c and/or that AP404 c offers the best characteristics, from the perspective of clientdevice 402. In such a case, client device 402 may initiate anotherroaming operation, thereby switching its access from AP 404 b to AP 404c.

FIG. 4D illustrates the concept of a mobility path 408, according tovarious embodiments. Based on the movement and wireless roamingoperations of client device 402 over time (e.g., between times T=t₀ andT=t₂), as depicted in FIG. 4A-4C, client device 402 can be viewed ashaving traversed mobility path 408. More specifically, mobility path 408may be a directed (or directionless) set of nodes/APs 404 through whichthe client device 402 roamed in the network. In this sense, roamingevents between the APs 404 can be viewed akin to hops between nodes in acommunication data path.

FIG. 4E illustrates an example of a roaming failure, according tovarious embodiments. Assume that the network assurance system hasidentified mobility path 408 by monitoring the roaming events for thevarious client devices in the network. Further, assume that clientdevice 412 first attached to AP 404 a and then to AP 404 b. Thus, thenetwork assurance system may determine that client device 412 is onmobility path 408 and will next roam to AP 404 c. However, instead ofroaming to AP 404 c, as expected, client device 412 newly attaches to AP404 d because of an inability to connect to AP 404 c. In this case,mobility path 408 can be said to have experienced a roaming failure 410,since client device 412 was unable to successfully roam to AP 404 c. Ifclient device 412 was participating in an online session at the time,roaming failure 410 could result in a loss of connectivity for clientdevice 412 and impact the user experience of the user of client device402. For example, if client device 412 is participating in an onlineconference, loss of network connectivity due to roaming failure 410could cause client device 412 to stop receiving the conference stream.

FIG. 5 illustrates an example architecture 500 for performing mobilitypath analysis in a network assurance system, according to variousembodiments. In general, architecture 500 may include any or all of thefollowing components: a mobility path analyzer 506 and/or a mobilitypath failure modeler 508. In various embodiments, the components ofarchitecture 500 may be implemented within a network assurance system,such as system 300 shown in FIG. 3. Accordingly, the components 506-508of architecture 500 shown may be implemented as part of cloud service302, as part of network data collection platform 304, and/or on one ormore network elements/entities 502 within the monitored network itself.For example, mobility path analyzer 506 and mobility path failuremodeler 508 may be implemented as part of machine learning-basedanalyzer 312, in some embodiments, as shown. Further, these componentsmay be implemented in a distributed manner or implemented as its ownstand-alone service, either as part of the local network underobservation or as a remote service. In addition, the functionalities ofthe components of architecture 500 may be combined, omitted, orimplemented as part of other processes, as desired.

During operation, a client device 504 may leverage one or more ofnetwork entities 502, to communicate wirelessly with the local network.For example, network entities 502 may include wireless APs, WLC,switches, routers, or the like, that provide network connectivity toclient device 504. In turn, network entities 502 may report informationregarding the roaming and other wireless conditions associated withclient device 504 to network data collection platform 304 as part ofdata 334. Network data collection platform 304 may then pass this dataon to cloud service 302 for analysis by machine learning (ML)-basedanalyzer 312.

A function of architecture 500 involves the notion of a mobility pathobjective function and a mobility failure metric for such paths. Withrespect to communicating data throughout a network, an objectivefunction may control the routing paths traversed by the data. Notably,in the context of IP or MPLS networks, routing metrics can be used inobjective functions (e.g., find the shortest constrained path given aspecific metric, which can reflect the bandwidth, jitter, link quality,etc.) and/or as a path constraint (e.g., prune paths for which a givenconstraint such as a color of minimum bandwidth, etc.), if notsatisfied. In a somewhat similar manner and in the context of a mobilitypath that represents the roaming transitions of a client device,architecture 500 may utilize mobility path failure metrics to representthe probability of a mobility/roaming failure along a given mobilitypath. In some embodiments, architecture 500 may also use these metricsas a constraint or in some objective functions, as detailed below.

Another functionality of architecture 500 is the computation of mobilitypath failure metrics which can be performed, in some embodiments, by asingle computational element, such as a PCE. In the case of a wirelessWi-Fi network, roaming events may be tracked by the WLC(s) (e.g.,network entities 502) to which the APs are connected. Note that a clientdevice, such as client device 504, may roam between APs connected todifferent WLCs, in which case, more than one WLC may be involved intracking roaming events. All roaming events are then collected (e.g., bydata collection platform 304) for each client device, to be able tocompute mobility paths.

Using the collected data, mobility path analyzer 506 may identify themobility paths that exist in the network. In some cases, these paths canbe distinguished in architecture 500 by user ID, thanks to theauthentication steps taken by the mobile devices (e.g., leveraging anISE), based on MAC addresses, or the like. In one embodiment, mobilitypath analyzer 506 may first compute the most frequent sub-trajectoriesof the client devices in the network. For example, mobility pathanalyzer 506 may apply a machine learning-based clustering approach tothe observed client trajectories, such as by using a time series of APstraversed by the clients and computing the most prominently occurringsequences of APs.

Said differently, mobility path analyzer 506 may transform the wirelesstraces from the monitored network into a mobility graph in which eachnode represents an AP, and the client device is represented as ahyper-edge on the graph. Using this graph notation, mobility pathanalyzer 506 may extract the trajectories of the client devices usersfrom wireless traces using heuristic and machine learning approaches.From the extracted trajectories, we leverage ML and other graphalgorithms to eliminate noisy paths where the device is oscillatingbetween few APs. In turn, mobility path analyzer 506 may identify themost-frequent sub-trajectories that have been traversed by a largenumber of clients. Data mining approaches such as frequent patternmining (e.g., TKS and TPS), can be used to extract most frequentsub-paths.

Referring briefly to FIG. 6, examples of mobility paths in threedimensions are shown. Notably, plot 600 illustrates in three dimensionsthe mobility paths 602 a-602 e observed during testing of a wirelessnetwork. In some cases, such as with mobility path 602 b and 602 d, theclient devices may tend to stay at the same z-coordinate, indicatingthat the client device is likely to roam along a mobility path on asingle floor. However, such as in the case of mobility path 602 e, theclient device may roam between APs with different z-coordinates,indicating that the user of the device may have traveled to a differentfloor. For each of these trajectories (e.g., links between APs in amobility path), architecture 500 may assess the observed roamingfailures and compute mobility failure metrics from these observations.For example, one insight from plot 600 is that the third floor (z=3) hasmany more failed paths concentrated in one area than that of the firstand second floors. In this way, architecture 500 can associate mobilityfailure metrics to the identified mobility paths.

For each sub-trajectory identified by mobility path analyzer 506,mobility path failure modeler 508 may compute mobility path failuremetrics based on the failure events observed over these sub-trajectories(e.g., by examining the distributions of failures, clusters of failuresthat are commonly occurring, etc.). In some embodiments, the mobilitypath failure metrics may also be client-specific or associated with agroup of clients. In other words, and in sharp contrast to data planemetrics that are independent of the client type, the mobility pathfailure metrics may also vary with the type of client (e.g., some clientdevices may have better reception than others, may be more prone toroam, etc.).

Referring again to FIG. 5, in various embodiments, architecture 500 maybe further configured to drive client device mobility based on themobility path failure metrics modeled by mobility path failure modeler508. In some embodiments, mobility path failure modeler 508 may leverageautomation & feedback controller 316 to upload the mobility pathinformation and failure metrics to network entities 502. For example,mobility path failure modeler 508 may upload this information to tablesmaintained by an AP, gateway, base station, or the like.

In general, given the knowledge of the frequent traversal paths andfailed paths of the client devices in the monitored network,architecture 500 can also trigger local reroutes in the mobility pathsof the client devices. In one embodiment, mobility path failure modeler508 predicts the approximate path of travel of client device 504 and itsfinal destination. Based on this predicted path, modeler 508 can computea list of the most effective alternative mobility paths for clientdevice 504 that are the most failure resistant. In one embodiment, thiscan be computed by running a shortest path algorithm on the path graphfrom mobility path analyzer 506. For example, the graph of the networkcan be computed with edges being weighted using the failure metrics. Inturn, modeler 508 can compute “weighted shortest paths” from the samesource and destination as that of the failed path. This will yieldprobable mobility paths with low failures.

Another approach would be to infer the best paths by using machinelearning and data-mining. For example, ML-based analyzer 312 can querythe client's history of all paths between the client's current AP andfinal destination. In turn, analyzer 312 can then mine these set ofpaths to infer the most-frequent paths which had low failures. Localrerouting and next-hop selection can be done based on the shortest pathinformation.

On joining the network, client device 504 may receive mobility pathinformation 510 from the AP to which client device 504 attaches. Forexample, mobility path information 510 may include a list of mobilitypaths along with their respective mobility path failure metrics for allpotential next hops/APs, to aid client device 504 in its roamingdecisions. In particular, client device 504 may use this mobility pathfailure metric, along with other characteristics, such as a measuredRSSI of another AP, to determine whether client device 504 should roamto that AP.

In some cases, mobility path information 510 may be provided to clientdevice 504 on expiration of a given timer, or when there are substantialchanges in the mobility path failure metrics. In another embodiment,network entities 502 may dynamically provide mobility path information510 on being explicitly requested by client device 504. For example,when client device 504 moves from AP_(x) to AP_(y), client device 504may be provided data indicative of the next hop AP and its associatedprobability of failure or may be provided a list of mobility paths withtheir respective mobility path failure metrics.

In yet another embodiment, the AP may anticipate which next hops arelikely to be visited by client device 504, by using machine learning topredict the mobility path that client device 504 is likely to take. Notethat the set of mobility path(s) that client device 504 is predicted totake may also be constrained to APs for which the expected signalquality from client device 504 will be above a given threshold. Forexample, the most probable, low failure paths can be inferred by miningclient device trajectories with a weight on each path that correspondsto the wireless failures observed on that path. Later, sequence mining,such as identifying the top-K subsequences, can be altered not to onlyaccount for the most probable sub-sequences, but also for the failuresalong this path. One adaption of such an approach may be to exclude theedges in the sub-sequence that have faced more than n %, in anotherembodiment. In turn, these sub-paths can be provided to client device504 as part of mobility path information 510.

In yet another mode of operation, client device 504 may explicitlyrequest mobility path information 510 from network entities 502 using acustom signaling extension, such as 802.1k/v in the case of a Wi-Finetwork. Such a request may indicate, for example, a requested set ofmobility paths that are constrained based on their mobility path failuremetrics. For example, if client device 504 is associated with a givenAP1, client device 504 may request a listing of all of the relevantmobility paths that have a mobility path failure metric that is lessthan a threshold value. On receiving this listing (e.g., as part ofmobility path information 510), client device 504 may use the listing toenhance its AP roaming decisions (e.g., by roaming along a constrainedmobility path that satisfies the specified failure metric constraint).Note that such a mode of operation introduces a new approach formobility in wireless networks whereby machine learning is used by acentral PCE to govern mobility, thus effectively influencing/overridinglocal decisions made by client device 504.

A further aspect of the techniques herein is the ability forarchitecture 500 to trigger mobility path reroutes for client device504. In particular, mobility path failure modeler 508 may determine thatthere is a high chance of mobility failure along the mobility path ofclient device 504. In turn, mobility path failure modeler 508 may pushmobility path information 510 to client device 504 that causes clientdevice 504 to deviate from its current mobility path to a differentmobility path.

By way of example, plots 700 and 710 in FIGS. 7A-7B illustrate examplesof the top-K subsequences/trajectories that affect clients in thenetwork. Notably, plot 700 illustrates a plot of the most failedsub-trajectories along a set of central nodes and plot 710 illustratesthe paths in a distributed manner. In other words, plot 700 providesinsight that only a few central nodes/APs in the network are responsiblefor many of the mobility path failures. In plot 710, however, thefailures are more distributed among different nodes. From plots 700-710,it can be seen how diverse mobility paths can be in a wireless networkand this insight can be leveraged by the network assurance system toassess and predict mobility path failures. As would be appreciated,failures can also be subdivided for purposes of predicting path failuremetrics, such as by distinguishing between authentication-relatedfailures and failures related to DHCP server timeouts.

In some embodiments, different approaches for updating mobility pathfailure metrics may be adopted according to the network topology, so asto avoid oscillation and instability. For example, rerouting clientdevices along different mobility paths may be load balanced acrossclients, so as to avoid rerouting too many clients along the same set ofAPs. In particular, rerouting clients to the same APs may triggeraddition mobility path failures because of the additional burden ofcontrol plane messages sent when roaming takes place.

Referring again to FIG. 5, another aspect of the techniques hereinrelates to performing next hop selection on client device 504, accordingto a set of active applications running on client device 504. Indeed, inmost end devices, the action of roaming is not always tied to the set ofactive applications, but is instead strictly a function of the wirelesssignal characteristics. In some embodiments, the roaming decision byclient device 504 may be governed, not only by the quality of themobility path measured by the path failure metric introduced herein, butalso based on one or more service level agreements (SLAs) of the activeapplication(s) on client device 504. For example, client device 504 maydecide not to roam when a potential next hop candidate (providing abetter signal strength) belongs to a mobility path with a low pathfailure metric and real-time applications are active on client device504 (e.g. a video call, etc.). In one embodiment, ML-based analyzer 312may compute the path failures and attach the application performance andthe failure attributes to the edges. Machine learning approaches, suchas trajectory clustering, can then be applied on top of these edges, toinfer the most-promising paths for client device 504 for a given set ofapplications.

In various embodiments, architecture 500 may also leverage feedbackregarding any detected roaming failures, to modify the models used inML-based analyzer 312. Such feedback may be provided by the WLC, as inthe case of Wi-Fi networks, but could also be provided by client device504, itself. In such cases, a protocol extension may be used to signalback when roaming has failed for client device 504. In either case,feedback can then be used to adjust the mobility path failure metricpredictions and potentially the underlying ML model used to compute suchmetrics. Techniques, such as reinforcement learning and active learning,can be used to strengthen the model based on the feedback.

FIG. 8 illustrates an example simplified procedure for triggering amobility path reroute by a client device in a network, in accordancewith one or more embodiments described herein. For example, anon-generic, specifically configured device (e.g., device 200) mayperform procedure 800 by executing stored instructions (e.g., process248) to provide a service to the network. The procedure 800 may start atstep 805, and continues to step 810, where, as described in greaterdetail above, the service may receive data indicative of roamingfailures along mobility paths in a network. In various embodiments, themobility paths represent ordered series of wireless access points viawhich wireless clients have accessed the network over time. For example,if client devices in the network are observed to roam from AP 1, to AP2, to AP 3, a corresponding mobility path may represent these APs asnodes and the roaming transitions as edges between the nodes.

At step 815, as detailed above, the service may use, based on the dataindicative of the roaming failures, a machine learning-based model toassociate mobility path failure metrics with portions of the mobilitypaths. In various embodiments, the mobility path failure metrics mayquantify the likelihood of a client device traversing a given mobilitypath experiencing a roaming failure between APs on the mobility path.

At step 820, the service may identify, for a first mobility path, analternate mobility path that has a lower mobility path failure metricthan that of the first mobility path. For example, if the mobility pathon which a client device is traversing has a mobility path failuremetric above a predefined threshold, this may indicate that the clientdevice is likely to experience a roaming failure on the current mobilitypath. In turn, the service may identify another mobility path that has alower path failure metric as an alternate mobility path for the clientdevice.

At step 825, the service may trigger a mobility path reroute for aparticular client device in the network on the first mobility path tothe alternate mobility path, as described in greater detail above. Insome embodiments, the service may trigger the mobility path reroute bysending mobility path information to the client device. For example,such information may indicate one or more mobility paths and theirassociated mobility path failure metrics, thereby allowing the clientdevice to locally switch to the other mobility path. In furtherembodiments, the service may receive an indication of a thresholdmobility path failure metric for the client device and trigger thereroute based on the first path's failure metric being below thisthreshold. For example, the types of applications running on the devicemay require a certain degree of continuous connectivity (e.g.,conferencing applications, etc.), thus requiring a mobility path havinga low chance of roaming failures. Procedure 800 then ends at step 830.

It should be noted that while certain steps within procedure 800 may beoptional as described above, the steps shown in FIG. 8 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, dramatically improve theuser experience in wireless networks, effectively avoiding a largenumber of roaming failures.

While there have been shown and described illustrative embodiments thatprovide for dynamic rerouting of wireless traffic based on input fromML-based mobility path analysis, it is to be understood that variousother adaptations and modifications may be made within the spirit andscope of the embodiments herein. For example, while certain embodimentsare described herein with respect to using certain models for purposesof predicting mobility path failure metrics, the models are not limitedas such and may be used for other functions, in other embodiments. Inaddition, while certain wireless protocols are shown, such as Wi-Fi,other suitable protocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: receiving, at a service,data indicative of roaming failures along mobility paths in a network,wherein the mobility paths represent ordered series of wireless accesspoints via which wireless clients have accessed the network over time;using, by the service and based on the data indicative of the roamingfailures, a machine learning-based model to associate mobility pathfailure metrics with portions of the mobility paths; identifying, by theservice and for a first mobility path, an alternate mobility path thathas a lower mobility path failure metric than that of the first mobilitypath; and triggering, by the service, a mobility path reroute for aparticular client device in the network on the first mobility path tothe alternate mobility path.
 2. The method as in claim 1, whereintriggering the mobility path reroute comprises: sending, by the service,data indicative of the alternate mobility path to the particular clientdevice via an access point in the network, wherein the data indicativeof the alternate mobility path causes the particular client device toroam to an access point along the alternate mobility path.
 3. The methodas in claim 2, wherein the data indicative of the alternate mobilitypath comprises at least one mobility path metric associated with thealternate mobility path.
 4. The method as in claim 1, wherein triggeringthe mobility path reroute comprises: receiving, at the service, athreshold mobility path failure metric from the particular clientdevice; and triggering, by the service, the mobility path reroute basedin part on a determination that the mobility path metric associated withthe alternate mobility path is lower than the received threshold.
 5. Themethod as in claim 1, further comprising: receiving, by the service andfrom the particular client device, an indication that the particularclient device experienced a roaming failure along the alternate mobilitypath; and modifying, by the service, the machine learning-based modelbased on the received indication that the particular client deviceexperienced a roaming failure along the alternate mobility path.
 6. Themethod as in claim 1, wherein a mobility failure corresponds to atraffic disruption experienced by one of the client devices caused by afailure to roam between wireless access points.
 7. The method as inclaim 1, wherein the mobility path reroute is triggered in part based onone or more service level agreements (SLAs) associated with one or moreactive applications on the particular client device.
 8. The method as inclaim 1, wherein the wireless access points are Wi-Fi access points. 9.The method as in claim 1, further comprising: computing, by the service,the mobility paths in the network in part by clustering trajectories ofthe client devices between the access points in the network.
 10. Anapparatus comprising: one or more network interfaces to communicate witha network; a processor coupled to the network interfaces and configuredto execute one or more processes; and a memory configured to store aprocess executable by the processor, the process when executedconfigured to: receive data indicative of roaming failures alongmobility paths in the network, wherein the mobility paths representordered series of wireless access points via which wireless clients haveaccessed the network over time; use, based on the data indicative of theroaming failures, a machine learning-based model to associate mobilitypath failure metrics with portions of the mobility paths; identify, fora first mobility path, an alternate mobility path that has a lowermobility path failure metric than that of the first mobility path; andtrigger a mobility path reroute for a particular client device in thenetwork on the first mobility path to the alternate mobility path. 11.The apparatus as in claim 10, wherein the apparatus triggers themobility path reroute by: sending data indicative of the alternatemobility path to the particular client device via an access point in thenetwork, wherein the data indicative of the alternate mobility pathcauses the particular client device to roam to an access point along thealternate mobility path.
 12. The apparatus as in claim 11, wherein thedata indicative of the alternate mobility path comprises at least onemobility path metric associated with the alternate mobility path. 13.The apparatus as in claim 10, wherein the apparatus triggers themobility path reroute by: receiving a threshold mobility path failuremetric from the particular client device; and triggering the mobilitypath reroute based in part on a determination that the mobility pathmetric associated with the alternate mobility path is lower than thereceived threshold.
 14. The apparatus as in claim 10, wherein theprocess when executed is further configured to: receive, from theparticular client device, an indication that the particular clientdevice experienced a roaming failure along the alternate mobility path;and modify the machine learning-based model based on the receivedindication that the particular client device experienced a roamingfailure along the alternate mobility path.
 15. The apparatus as in claim10, wherein a mobility failure corresponds to a traffic disruptionexperienced by one of the client devices caused by a failure to roambetween wireless access points.
 16. The apparatus as in claim 10,wherein the mobility path reroute is triggered in part based on one ormore service level agreements (SLAs) associated with one or more activeapplications on the particular client device.
 17. The apparatus as inclaim 10, wherein the wireless access points are Wi-Fi access points.18. The apparatus as in claim 10, wherein the process when executed isfurther configured to: computing, by the service, the mobility paths inthe network in part by clustering trajectories of the client devicesbetween the access points in the network.
 19. A tangible,non-transitory, computer-readable medium storing program instructionsthat cause a device to execute a process comprising: receiving dataindicative of roaming failures along mobility paths in a network,wherein the mobility paths represent ordered series of wireless accesspoints via which wireless clients have accessed the network over time;using, based on the data indicative of the roaming failures, a machinelearning-based model to associate mobility path failure metrics withportions of the mobility paths; identifying, for a first mobility path,an alternate mobility path that has a lower mobility path failure metricthan that of the first mobility path; and triggering a mobility pathreroute for a particular client device in the network on the firstmobility path to the alternate mobility path.
 20. The computer-readablemedium as in claim 19, wherein the process further comprises: receiving,from the particular client device, an indication that the particularclient device experienced a roaming failure along the alternate mobilitypath; and modifying the machine learning-based model based on thereceived indication that the particular client device experienced aroaming failure along the alternate mobility path.